Cloud-based Machine Learning Framework for Residential HVAC Control System

Occupant behavior prediction has become a significant part of smart building technologies. This research emphasizes on HVAC (Heating Ventilation and Air Conditioning) system as its operation is crucial in terms of both energy usage and user comfort. In previous researches, comfort prediction is developed using the filled questionnaires from occupants. The prediction output is employed to control the mechanical components of the system. On/Off control is mostly developed using rule-based distance triggering that turns on/off the system when occupants enter/exit a specific perimeter. This article proposes a plug-and-play HVAC control system with an adaptive cloud machine learning (ML) framework that utilizes room current ambient conditions together with the historical adjustment log to automatically adjust room temperature according to the user comfort using Support Vector Classification (SVC). This system can also turn on air conditioner automatically using predictive control which implements GPS location and the occupant's data on the Artificial Neural Network (ANN). Furthermore, cloud deployment can solve problems in processing power and storage of an end device, also provide scalability for future development. Findings show that an adaptive algorithm in the ML framework can perform well, even when the occupant's behavior changes slowly.